10 March 2017 A model based on temporal dynamics of fixations for distinguishing expert radiologists' scanpaths
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Abstract
This study investigated a model which distinguishes expert radiologists from less experienced radiologists based on features describing spatio-temporal dynamics of their eye movement during interpretation of digital mammograms. Eye movements of four expert and four less experienced radiologists were recorded during interpretation of 120 two-view digital mammograms of which 59 had biopsy proven cancers. For each scanpath, a two-dimensional recurrence plot, which represents the radiologist’s refixation pattern, was generated. From each plot, six features indicating the spatio-temporal dynamics of fixations were extracted. The first feature measured the percentage of recurrent fixations; the second indicated the percentage of recurrent fixations which was fixated later in several consecutive fixations; the third measured the percentage of recurrent fixations that form a repeated sequence of fixations and the fourth assessed whether the recurrent fixations were occurring sequentially close together. The number of switches between the two mammographic views was also measured, as was the average number of consecutive fixations in each view before switching. These six features along with total time on case and average fixation duration were fed into a support vector machine whose performance was evaluated using 10-fold cross validation. The model achieved a sensitivity of 86.3% and a specificity of 85.2% for distinguishing experts’ scanpaths. The obtained result suggests that spatio-temporal dynamics of eye movements can characterize expertise level and has potential applications for monitoring the development of expertise among radiologists as a result of different training regimes and continuing education schemes.
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Ziba Gandomkar, Kevin Tay, Patrick C. Brennan, Claudia Mello-Thoms, "A model based on temporal dynamics of fixations for distinguishing expert radiologists' scanpaths", Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 1013606 (10 March 2017); doi: 10.1117/12.2254527; https://doi.org/10.1117/12.2254527
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